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**Chapter 9 Greedy Technique**

Copyright © 2007 Pearson Addison-Wesley. All rights reserved.

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Greedy Technique Constructs a solution to an optimization problem piece by piece through a sequence of choices that are: feasible, i.e. satisfying the constraints locally optimal (with respect to some neighborhood definition) greedy (in terms of some measure), and irrevocable For some problems, it yields a globally optimal solution for every instance. For most, does not but can be useful for fast approximations. We are mostly interested in the former case in this class. Defined by an objective function and a set of constraints

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**Applications of the Greedy Strategy**

Optimal solutions: change making for “normal” coin denominations minimum spanning tree (MST) single-source shortest paths simple scheduling problems Huffman codes Approximations/heuristics: traveling salesman problem (TSP) knapsack problem other combinatorial optimization problems

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**Change-Making Problem**

Given unlimited amounts of coins of denominations d1 > … > dm , give change for amount n with the least number of coins Example: d1 = 25c, d2 =10c, d3 = 5c, d4 = 1c and n = 48c Greedy solution: Greedy solution is optimal for any amount and “normal’’ set of denominations may not be optimal for arbitrary coin denominations Q: What are the objective function and constraints? <1, 2, 0, 3> Ex: Prove the greedy algorithm is optimal for the above denominations. For example, d1 = 25c, d2 = 10c, d3 = 1c, and n = 30c

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**Minimum Spanning Tree (MST)**

Spanning tree of a connected graph G: a connected acyclic subgraph of G that includes all of G’s vertices Minimum spanning tree of a weighted, connected graph G: a spanning tree of G of the minimum total weight Example: c d b a 6 2 4 3 1 c d b a 6 4 1 c d b a 2 3 1

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Prim’s MST algorithm Start with tree T1 consisting of one (any) vertex and “grow” tree one vertex at a time to produce MST through a series of expanding subtrees T1, T2, …, Tn On each iteration, construct Ti+1 from Ti by adding vertex not in Ti that is closest to those already in Ti (this is a “greedy” step!) Stop when all vertices are included

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**Example c d b a c d b a c d b a c d b a c d b a 4 2 6 1 3 4 2 6 1 3 4**

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**Notes about Prim’s algorithm**

Proof by induction that this construction actually yields an MST (CLRS, Ch. 23.1). Main property is given in the next page. Needs priority queue for locating closest fringe vertex. The Detailed algorithm can be found in Levitin, P. 310. Efficiency O(n2) for weight matrix representation of graph and array implementation of priority queue O(m log n) for adjacency lists representation of graph with n vertices and m edges and min-heap implementation of the priority queue

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**The Crucial Property behind Prim’s Algorithm**

Claim: Let G = (V,E) be a weighted graph and (X,Y) be a partition of V (called a cut). Suppose e = (x,y) is an edge of E across the cut, where x is in X and y is in Y, and e has the minimum weight among all such crossing edges (called a light edge). Then there is an MST containing e. Y y x X

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**Another greedy algorithm for MST: Kruskal’s**

Sort the edges in nondecreasing order of lengths “Grow” tree one edge at a time to produce MST through a series of expanding forests F1, F2, …, Fn-1 On each iteration, add the next edge on the sorted list unless this would create a cycle. (If it would, skip the edge.)

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**Example c c c a a a d d d b b b c c c a a a d d d b b b 4 4 4 1 1 1 6**

2 6 1 3 c d b a 4 2 6 1 3 c d b a 4 2 6 1 3 c d b a 4 2 6 1 3 c d b a 2 6 1 3 c d b a 2 1 3

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**Notes about Kruskal’s algorithm**

Algorithm looks easier than Prim’s but is harder to implement (checking for cycles!) Cycle checking: a cycle is created iff added edge connects vertices in the same connected component Union-find algorithms – see section 9.2 Runs in O(m log m) time, with m = |E|. The time is mostly spent on sorting.

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**Minimum spanning tree vs. Steiner tree**

1 a c a c s vs 1 1 b d b d 1 In general, a Steiner minimal tree (SMT) can be much shorter than a minimum spanning tree (MST), but SMTs are hard to compute.

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**Shortest paths – Dijkstra’s algorithm**

Single Source Shortest Paths Problem: Given a weighted connected (directed) graph G, find shortest paths from source vertex s to each of the other vertices Dijkstra’s algorithm: Similar to Prim’s MST algorithm, with a different way of computing numerical labels: Among vertices not already in the tree, it finds vertex u with the smallest sum dv + w(v,u) where v is a vertex for which shortest path has been already found on preceding iterations (such vertices form a tree rooted at s) dv is the length of the shortest path from source s to v w(v,u) is the length (weight) of edge from v to u

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**Example Tree vertices Remaining vertices**

b 4 e 3 7 6 2 5 c Example 4 d d Tree vertices Remaining vertices a(-,0) b(a,3) c(-,∞) d(a,7) e(-,∞) a b d 4 c e 3 7 6 2 5 4 b(a,3) c(b,3+4) d(b,3+2) e(-,∞) b c 3 6 2 5 a d e 7 4 d(b,5) c(b,7) e(d,5+4) 4 b c 3 6 2 5 a d 7 e 4 4 c(b,7) e(d,9) b c 3 6 2 5 a d e 7 4 e(d,9)

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**Notes on Dijkstra’s algorithm**

Correctness can be proven by induction on the number of vertices. Doesn’t work for graphs with negative weights (whereas Floyd’s algorithm does, as long as there is no negative cycle). Can you find a counterexample for Dijkstra’s algorithm? Applicable to both undirected and directed graphs Efficiency O(|V|2) for graphs represented by weight matrix and array implementation of priority queue O(|E|log|V|) for graphs represented by adj. lists and min-heap implementation of priority queue Don’t mix up Dijkstra’s algorithm with Prim’s algorithm! More details of the algorithm are in the text and ref books. We prove the invariants: (i) when a vertex is added to the tree, its correct distance is calculated and (ii) the distance is at least those of the previously added vertices.

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Coding Problem Coding: assignment of bit strings to alphabet characters Codewords: bit strings assigned for characters of alphabet Two types of codes: fixed-length encoding (e.g., ASCII) variable-length encoding (e,g., Morse code) Prefix-free codes (or prefix-codes): no codeword is a prefix of another codeword Problem: If frequencies of the character occurrences are known, what is the best binary prefix-free code? E.g. We can code {a,b,c,d} as {00,01,10,11} or {0,10,110,111} or {0,01,10,101}. E.g. if P(a) = 0.4, P(b) = 0.3, P(c) = 0.2, P(d) = 0.1, then the average length of code #2 is * * * = 1.9 bits It allows for efficient (online) decoding! E.g. consider the encoded string (msg) … The one with the shortest average code length. The average code length represents on the average how many bits are required to transmit or store a character.

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**Huffman codes represents {00, 011, 1}**

Any binary tree with edges labeled with 0’s and 1’s yields a prefix-free code of characters assigned to its leaves Optimal binary tree minimizing the average length of a codeword can be constructed as follows: Huffman’s algorithm Initialize n one-node trees with alphabet characters and the tree weights with their frequencies. Repeat the following step n-1 times: join two binary trees with smallest weights into one (as left and right subtrees) and make its weight equal the sum of the weights of the two trees. Mark edges leading to left and right subtrees with 0’s and 1’s, respectively. 1 represents {00, 011, 1}

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**Example character A B C D _ frequency 0.35 0.1 0.2 0.2 0.15**

codeword average bits per character: 2.25 for fixed-length encoding: 3 compression ratio: (3-2.25)/3*100% = 25%

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Greedy Technique The first key ingredient is the greedy-choice property: a globally optimal solution can be arrived at by making a locally optimal (greedy)

Greedy Technique The first key ingredient is the greedy-choice property: a globally optimal solution can be arrived at by making a locally optimal (greedy)

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